Publication: Production planning with flexible manufacturing systems under demand uncertainty
dc.contributor.author | Elyasi, M. | |
dc.contributor.author | Özener, Başak Altan | |
dc.contributor.author | Ekici, Ali | |
dc.contributor.author | Özener, Okan Örsan | |
dc.contributor.department | Economics | |
dc.contributor.department | Industrial Engineering | |
dc.contributor.ozuauthor | ÖZENER, Başak Altan | |
dc.contributor.ozuauthor | EKİCİ, Ali | |
dc.contributor.ozuauthor | ÖZENER, Okan Örsan | |
dc.date.accessioned | 2024-02-20T11:14:33Z | |
dc.date.available | 2024-02-20T11:14:33Z | |
dc.date.issued | 2024 | |
dc.description.abstract | This paper delves into the impacts of an ongoing global crisis on the resilience of supply chains. Furthermore, it proposes measures to address and mitigate the disruptions caused by the prevailing uncertainties. For example, while the economy has started to recover after the pandemic and demand has increased, companies have not fully returned to their pre-pandemic levels. To enhance their supply chain resilience and effectively manage disruptions, one viable strategy is the implementation of flexible/hybrid manufacturing systems. This research is motivated by the specific requirements of Vestel Electronics, a household appliances company, which seeks a flexible/hybrid manufacturing production setup involving dedicated machinery to meet regular demand and the utilisation of flexible manufacturing system (FMS) to handle surges in demand. We employ a scenario-based approach to model demand uncertainty, enabling the company to make immediate and adaptive decisions that take advantage of the cost-effectiveness of standard production and the responsiveness of FMS. To solve the problem, we propose a heuristic algorithm based on column generation. The numerical results demonstrate that our optimisation model provides solutions with an average optimality gap of less than 6% while also reducing the average cost of standard production schemes without FMS by over 12%. | en_US |
dc.identifier.doi | 10.1080/00207543.2023.2288722 | en_US |
dc.identifier.endpage | 170 | en_US |
dc.identifier.issn | 0020-7543 | en_US |
dc.identifier.issue | 1-2 | en_US |
dc.identifier.scopus | 2-s2.0-85178206713 | |
dc.identifier.startpage | 157 | en_US |
dc.identifier.uri | http://hdl.handle.net/10679/9178 | |
dc.identifier.uri | https://doi.org/10.1080/00207543.2023.2288722 | |
dc.identifier.volume | 62 | en_US |
dc.identifier.wos | 001110094900001 | |
dc.language.iso | eng | en_US |
dc.peerreviewed | yes | en_US |
dc.publicationstatus | Published | en_US |
dc.publisher | Taylor & Francis | en_US |
dc.relation.ispartof | International Journal of Production Research | |
dc.relation.publicationcategory | International Refereed Journal | |
dc.rights | restrictedAccess | |
dc.subject.keywords | Flexible manufacturing systems | en_US |
dc.subject.keywords | Inventory control | en_US |
dc.subject.keywords | Production planning | en_US |
dc.subject.keywords | Scheduling | en_US |
dc.subject.keywords | Stochastic optimisation | en_US |
dc.title | Production planning with flexible manufacturing systems under demand uncertainty | en_US |
dc.type | article | en_US |
dspace.entity.type | Publication | |
relation.isOrgUnitOfPublication | 2afe80e3-623c-4807-a57e-2ce75845ccea | |
relation.isOrgUnitOfPublication | 5dd73c02-fd2d-43e0-9a23-71bab9ae0b6b | |
relation.isOrgUnitOfPublication.latestForDiscovery | 2afe80e3-623c-4807-a57e-2ce75845ccea |
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